Energy-efficiency maximization for fixed-wing UAV-enabled relay network with circular trajectory

With the development of Unmanned Aerial Vehicles (UAVs), the applications of UAVs have been extensively explored. In the field of wireless communications, the relay nodes are often used to extend network coverage. However, traditional fixed ground relays cannot be flexibly deployed due to their low heights and fixed locations. Hence, deploying UAV as relay node is a promising solution and has become a research hotspot. In this paper, we consider an UAV-enabled relaying network in which a fixed-wing UAV is deployed between the Base Station (BS) and Ground Users (GUs). We study the energy-efficiency gap between the link “BS-UAV-GUs” and the link “BS-GUs”, and jointly optimize UAV relay transmission power and flight radius to achieve the highest energy-efficiency. Firstly, the UAV/BS-GUs channels models and the UAV energy consumption model are built. Secondly, the optimization objective function is formulated to maximize the energy-efficiency gap. Then, the solution of the optimization problem is divided into a two-step iteration process, in which the UAV relay transmission power and flight radius are adjusted to maximize the energy-efficiency gap. Finally, the experimental results under different simulation scenarios (such as cities, forests, deserts, oceans, etc.) are shown to illustrate the effectiveness of the proposed algorithm. The results show that the proposed algorithm can always find the optimal UAV relay transmission power and flight radius settings, and achieve the largest energy-efficiency gap. The convergency speed of the proposed algorithm is fast, and can obtain the optimal solution within only a few iterations.     

Optimal trajectory and downlink power control for multi-type UAV aerial base stations

Unmanned Aerial Vehicles (UAVs) enabled Aerial Base Stations (UABSs) have been studied widely in future communications. However, there are a series of challenges such as interference management, trajectory design and resource allocation in the scenarios of multi-UAV networks. Besides, different performances among UABSs increase complexity and bring many challenges. In this paper, the joint downlink transmission power control and trajectory design problem in multi-type UABSs communication network is investigated. In order to satisfy the signal to interference plus noise power ratio of users, each UABS needs to adjust its position and transmission power. Based on the interactions among multiple communication links, a non-cooperative Mean-Field-Type Game (MFTG) is proposed to model the joint optimization problem. Then, a Nash equilibrium solution is solved by two steps: first, the users in the given area are clustered to get the initial deployment of the UABSs; second, the Mean-Field Q (MFQ)-learning algorithm is proposed to solve the discrete MFTG problem. Finally, the effectiveness of the approach is verified through the simulations, which simplifies the solution process and effectively reduces the energy consumption of each UABS.     

无人机中继平台覆盖区域统计模型简

 针对中继信号覆盖区域确定性模型没有考虑信道随机衰落影响的问题,提出了一种基于中断概率的无人机（UAVs）中继平台信号覆盖半径统计模型。通过将中继链路建模为包含路径损耗、阴影衰落和多径衰落的复合分布模型,推导获得中继信号覆盖半径的概率分布,并给出一种利用中断概率数值求解覆盖半径的方法。仿真结果表明,信道衰落对覆盖半径影响非常大,当中断概率小于10%时,覆盖半径仅为无衰落信道时的一半。研究结果对移动自组织网络（MANETs）中的中继网络的无人机最优布置、飞行策略以及网络性能评估等具有重要的参考价值。     

无人机中继平台覆盖区域统计模型

针对中继信号覆盖区域确定性模型没有考虑信道随机衰落影响的问题,提出了一种基于中断概率的无人机（UAVs）中继平台信号覆盖半径统计模型。通过将中继链路建模为包含路径损耗、阴影衰落和多径衰落的复合分布模型,推导获得中继信号覆盖半径的概率分布,并给出一种利用中断概率数值求解覆盖半径的方法。仿真结果表明,信道衰落对覆盖半径影响非常大,当中断概率小于10%时,覆盖半径仅为无衰落信道时的一半。研究结果对移动自组织网络（MANETs）中的中继网络的无人机最优布置、飞行策略以及网络性能评估等具有重要的参考价值。     

基于多机协作的认知无人机网络能效联合优化

针对无人机（UAV）通信网络中频谱资源紧缺的问题,构建基于认知无线电的多无人机通信网络,通过多机协作频谱感知有效探索授权频谱。提出一种基于Bisection算法的迭代算法,通过联合优化感知时间和判决门限对构建的复杂非凸问题求解,显著提高了无人机次级认知网络的能量效率（EE）。分析了无人机飞行过程中能效的变化情况,仿真结果表明,存在最优感知时间使能效获得最大值,且判决门限的选择会影响该能效最优值;提出的高能效迭代算法具有较好收敛性,有效提高了认知无人机网络的能量利用率。     

Topology tracking of dynamic UAV wireless networks

Wireless network is the communication foundation that supports the intelligentization of Unmanned Aerial Vehicle(UAV) swarm. The topology of UAV communication network is the key to understanding and analyzing the behavior of UAV swarm, thus supporting the further prediction of UAV operations. However, the UAV swarm network topology varies over time due to the high mobility and diversified mission requirements of UAVs. Therefore, it is important but challenging to research dynamic topology infere...     

基于能量优化的无人机机动轨迹生成方法

基于能量优化的无人机机动能够使无人机在不失去决定性位置优势的情况下获取相对于对手的能量优势，足够的能量优势可转换为有效的位置优势，有利于无人机在空战中随时改出当前机动并投入下一机动动作，对于无人机获取空战胜利至关重要。开展了基于能量优化的无人机机动轨迹生成方法研究，通过在无人机机动飞行包线内设计合适的机动指令，使得无人机能量性能指标最优。以上升转弯机动为例进行了机动轨迹生成的详细设计，并与常规上升转弯机动轨迹生成结果进行对比，仿真结果显示所设计的机动动作完成时间缩短了28.6%~83.8%，总能量变化减少了64.7%~70.1%，实现了无人机机动飞行的能量优化。     

Evolution analysis of a UAV real-time operating system from a network perspective

With the flourishing development of Unmanned Aerial Vehicles (UAVs), the mission tasks of UAVs have become more and more complex. Consequently, a Real-Time Operating System (RTOS) that provides operating environments for various mission services on these UAVs has become crucial, which leads to the necessity of having a deep understanding of an RTOS. In this paper, an empirical study is conducted on FreeRTOS, a commonly used RTOS for UAVs, from a complex network perspective. A total of 85 releases of FreeRTOS, from V2.4.2 to V10.0.0, are modeled as directed networks, in which the nodes represent functions and the edges denote function calls. It is found that the size of the FreeRTOS network has grown almost linearly with the evolution of the versions, while its main core has evolved steadily. In addition, a k-core analysis-based metric is proposed to identify major functionality changes of FreeRTOS during its evolution. The result shows that the identified versions are consistent with the version change logs. Finally, it is found that the clustering coefficient of the Linux OS scheduler is larger than that of the FreeRTOS scheduler. In conclusion, the empirical results provide useful guidance for developers and users of UAV RTOSs.     

空时分组编码的无人机中继通信航迹规划方法

无人机(UAV)中继通信是实现远距离点对点无线通信的一种重要技术手段。为提高无人机中继通信系统链路传输的可靠性,提出基于空时分组编码的无人机放大转发中继通信传输方案,并基于双跳链路遍历容量最大化的准则给出了无人机最佳航迹规划方法,并进一步利用FM-EM算法给出了基于空时分组编码无人机中继通信系统的中断概率及遍历信道容量计算公式。计算机仿真表明:提出的方法显著优于传统的单发单收(SISO)无人机中继通信系统。     

Optimal Cooperative Sensing using a Team of UAVs

The authors investigates the joint optimal estimation of both the position and velocity of a ground moving target (GMT) using pulse Doppler radars on-board unmanned aerial vehicles (UAVs). The problem of cooperative estimation using a UAV team and the optimization of the team's configuration to achieve optimal GMT position and velocity estimates are addressed. Based on the Cramer-Rao bound, the minimum achievable error variance of the GMT position and velocity estimates is derived. The expression of the minimum achievable estimation error variance for unbiased estimation provided by the Cramer-Rao bound is minimized yielding the optimal configuration of the UAV team. Our solution is complete in that it addresses various GMT tracking scenarios and an arbitrary number of UAVs. Optimal sensor geometries for typical applications are illustrated     

第一视角飞行模式设计与无人机着陆试验研究

设计了一种第一视角飞行模式,同时观察图像与导航数据来控制无人机飞行并实现着陆.第一视角飞行是一种基于无人机上加装无线图像传输设备,在地面看屏幕操控无人机的操作模式.无人机操纵员通过观察前视探测器拍摄的图像和导航数据控制遥控器,地面控制站捕捉遥控器的输出信号并把该信号通过数据链传至机载飞控系统,从而实现对无人机的控制.该飞行模式的实现,提高了无人机飞行的机动性,突破了小型无人机只能在本场着陆的限制,使无人机可以在远离本场情况下安全着陆.     

Ergodic sum rate for uplink NOMA transmission in satellite-aerial-ground integrated networks

The application of Non-Orthogonal Multiple Access (NOMA) technology into satellite-aerial-ground integrated networks can meet the requirements of ultra-high rate and massive connectivity for the Sixth-Generation (6G) communication systems. We consider an uplink NOMA scenario for such a satellite-aerial-ground integrated network where multiple users communicate with satellite under the help of an Unmanned Aerial Vehicle (UAV) as an aerial relay equipped with a phased array. Supposing that buffer-aided decode-and-forward protocol is adopted at the UAV relay, we first formulate an optimization problem to maximize Ergodic Sum Rate (ESR) of the considered system subject to individual power constraint and quality-of-service constraint of each user. Then, with known imperfect channel state information of each user, we propose a joint power allocation and robust Beam Forming (BF) iterative algorithm to maximize ESR for the user-to-UAV link. Besides, to take the advantages of Free-Space Optical (FSO) and millimeter Wave (mmWave) communications, we present a switch-based hybrid FSO/mmWave scheme and a robust BF algorithm for the UAV-to-satellite link to achieve higher rate. Moreover, a closed-form ESR expression is derived. Finally, the effectiveness and correctness of the proposed solutions are verified by numerical simulations, and the performance evaluation results show that the proposed solutions not only achieve performance enhancement and robustness, but also outperform the orthogonal multiple access significantly.     

基于MAS的无人机编队飞行智能优化控制

 针对无人机编队飞行的实际背景和限制条件,提出了一种基于多智能体系统（MAS）技术的编队飞行的智能优化控制策略和实现算法。利用多Agent之间的交互作用,以灵活便捷的方式进行各单机之间的协同优化,从而可实现多架无人机的自主编队飞行。考虑到leader-follower原理的支配机制,对面向编队飞行的僚机控制律与实现算法进行了重点研究,设计了僚机编队控制器,通过仿真实验验证了其鲁棒性和稳定性。最后针对编队队形的变换与躲避威胁等特殊情况对无人机编队单元之间的协调优化进行了仿真验证和分析。     

基于深度学习的无人机航拍目标检测研究综述

目标检测是提高无人机（UAV）感知能力的关键技术之一,其研究对于无人机的应用有着重要意义。与基于手工特征的传统方法相比,基于卷积神经网络的深度学习方法具有强大的特征学习和表达能力,成为目前目标检测任务的主流算法。近年来,目标检测技术已经在自然场景图像上取得了一系列突破性进展,在无人机领域的研究也逐渐成为热点。首先系统阐述了基于深度学习的目标检测算法的研究进展,并总结了相关算法的优缺点。对常见的航空影像数据集进行了梳理并介绍了迁移学习的方法;从无人机影像背景复杂、目标较小、视场大、目标具有旋转性的特点出发,对无人机目标检测在近期的研究进行了归纳和分析。最后讨论了存在的问题和未来可能的发展方向。     

基于 ABMS的无人机编队协同制空任务效能评估

无人机是海空作战的重要力量,制空作战是未来无人机的重要任务之一。为研究无人机编队协同制空作战的制胜机理,评估无人机编队协同制空作战效能:首先,对无人机编队协同制空任务进行分析,明确典型作战过程;然后,采用基于 Agent的建模与仿真(Agent-basedmodelingandsimulation,ABMS)方法,构建攻击型无人机、预警型无人机和空空导弹的 Agent模型,开展多无人机协同制空作战仿真和效能分析。结果表明,无人机探测距离、编队内攻击型无人机的数量,以及编队内有无预警型无人机,这些都将对作战效能产生重要影响,该结论可为无人机协同作战运用提供参考。     

Optimization of beamforming and path planning for UAV-assisted wireless relay networks

Recently, unmanned aerial vehicles （UAVs） acting as relay platforms have attracted considerable attention due to the advantages of extending coverage and improving connectivity for long-range communications. Specifically, in the scenario where the access point （AP） is mobile, a UAV needs to find an efficient path to guarantee the connectivity of the relay link. Motivated by this fact, this paper proposes an optimal design for beamforming （BF） and UAV path planning. First of all, we study a dual-hop amplify-and-forward （AF） wireless relay network, in which a UAV is used as relay between a mobile AP and a fixed base station （BS）. In the network, both of the AP and the BS are equipped with multiple antennas, whereas the UAV has a single antenna. Then, we obtain the output signal~to-noise ratio （SNR） of the dual-hop relay network. Based on the criterion of maximizing the output SNR, we develop an optimal design to obtain the solution of the optimal BF weight vector and the UAV heading angle. Next, we derive the closed-form outage probability （OP） expression to investigate the performance of the dual-hop relay network conveniently. Finally, computer simulations show that the proposed approach can obtain nearly optimal flying path and OP performance, indicating the effectiveness of the proposed algorithm. Furthermore, we find that increasing the antenna number at the BS or the maximal heading angle can significantly improve the performance of the considered relay network.     

频率选择性衰落信道DS-CDMA无人机中继通信系统航迹规划

无人机中继通信是实现远距离无线通信的一种重要技术手段，无人机的飞行航迹对无人机中继通信系统的链路传输可靠性存在显著的影响，在频率选择性衰落信道环境下研究了基于直序列码分多址（DS-CDMA）的无人机中继通信系统的航迹优化的问题。首先，给出了基于DS-CDMA的译码转发无人机中继通信系统的模型，并理论分析给出无人机中继通信系统的链路中断概率及平均误码率计算公式，以此为基础，基于链路中断概率最小化准则提出了中继无人机的航迹规划方法，最后通过仿真验证了所提出方法的正确性与有效性。研究表明：最大比值合并DS-CDMA无人机中继通信系统可充分获取频率选择性衰落信道提供的分集增益，显著改善链路传输的可靠性。     

无线传感器网络的异构性研究

随着无线通信技术和微电子技术的快速发展,低成本、高度集成和自组织的无线传感器网络将会逐渐普及.首先总结归纳了无线传感器网络异构性的几种表现形式,如计算能力异构性、节点能量异构性、链路异构性及网络协议异构性,然后介绍了异构无线传感器网络层次网络体系结构和两种适用于无线传感器网络的标准.最后对无线传感器网络的安全问题进行简单探讨.     

多仓库下物流无人机协同配送方法

无人机以其快捷、低成本优势,在物流配送中可以实现高效的包裹配送,但也存在着运行时间短、载重不足等缺点。针对当前配送建模考虑因素不够全面的问题,构建了基于能耗变化、混合时间窗和同时取送货的多仓库物流无人机配送模型,以实现配送经济成本最低。与经典的多基地车辆路径问题相比,文中研究的问题没有限制无人机出发和返回的仓库,旨在最大限度地减少无人机的数量和所有无人机行驶的总距离。为进一步优化物流无人机配送成本,针对遗传算法(GeneticAlgorithm,GA)寻优能力较差的问题,引入大规模领域搜索算法(LargeNeighbor-hoodSearchAlgorithm,LNS)作为局部搜索算子,进而提出基于改进 GA(ImprovedGA,IGA)的物流无人机协同配送算法。经仿真测试以及 Solomn标准数据验证,该算法较传统 GA在降低配送成本方面成效明显。